Abstract
Parkinson’s disease is a neural degenerative disease. It slowly progresses from mild to severe stage, resulting in the degeneration of dopamine cells of neurons. Due to the deficiency of dopamine cells in the brain, it leads to a motor (tremor, slowness, impaired posture) and non-motor (speech, olfactory) defects in the body. Early detection of Parkinson’s disease is a difficult chore as the symptoms of disease appear overtime. However, different diagnostic systems have contributed towards disease detection by considering gait, tremor and speech characteristics. Recent work has shown that speech impairments can be considered as a possible predictor for Parkinson’s disease classification and remains an open research area. The speech signals show major differences and variations for Parkinson patients as compared to normal human beings. Therefore, variation in speech should be modeled using acoustic features to identify these variations. In this research, we propose three methods- the first method employs a transfer learning-based approach using spectrograms of speech recordings, the second method evaluates deep features extracted from speech spectrograms using machine learning classifiers and the third method evaluates simple acoustic feature of recordings using machine learning classifiers. The proposed frameworks are evaluated on a Spanish dataset pc-Gita. The results show that the second framework shows promising results with deep features. The highest 99.7% accuracy on vowel $\backslash \text{o}\backslash $ and read text is observed using a multilayer perceptron. Whereas 99.1% accuracy observed on vowel $\backslash \text{i}\backslash $ deep features using random forest. The deep feature-based method performs better as compared to simple acoustic features and transfer learning approaches. The proposed methodology outperforms the existing techniques on the pc-Gita dataset for Parkinson’s disease detection.
Highlights
Parkinson’s disease (PD) is a slowly progressing neural degenerative disease
We proposed a simple acoustic features based method and considered a pre-trained convolution neural network architecture [13], [14] Alexnet model for deep feature extraction and detection of PD
The main contributions of our research for PD detection using speech signals are the following: 1. We propose a spectrogram based approach to extract deep feature to distinguish PD patients from healthy
Summary
Parkinson’s disease (PD) is a slowly progressing neural degenerative disease. The main origin of the disease is still unknown [1]. L. Zahid et al.: Spectrogram-Based Deep Feature Assisted Computer-Aided Diagnostic System for PD associated with movements and are more perceptible as compared to non-motor symptoms [3]. Speech signals are usually considered as one of the main methods to diagnose Parkinson’s disease. In [6] authors accounted that articulation, intelligibility, prosody features of speech signal shows promising results in the detection of PD. This research work contemplates speech recordings using spectrograms and acoustic features. We proposed a simple acoustic features based method and considered a pre-trained convolution neural network architecture [13], [14] Alexnet model for deep feature extraction and detection of PD. To evaluate the performance of the proposed methods, Parkinson’s disease speech recordings from PC-GITA [15] dataset are used.
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